Overview

Dataset statistics

Number of variables13
Number of observations77947
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 MiB
Average record size in memory104.0 B

Variable types

NUM12
CAT1

Reproduction

Analysis started2020-11-20 08:59:02.016536
Analysis finished2020-11-20 08:59:44.382510
Duration42.37 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Value.7 is highly correlated with ValueHigh correlation
Value is highly correlated with Value.7High correlation
Value.9 is highly skewed (γ1 = 22.44607899) Skewed
Benutzerdefiniert has unique values Unique
Value.9 has 70053 (89.9%) zeros Zeros
Running App ID has 23661 (30.4%) zeros Zeros

Variables

Benutzerdefiniert
Real number (ℝ≥0)

UNIQUE

Distinct count77947
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38973.0
Minimum0
Maximum77946
Zeros1
Zeros (%)< 0.1%
Memory size609.0 KiB
2020-11-20T09:59:44.464473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3897.3
Q119486.5
median38973
Q358459.5
95-th percentile74048.7
Maximum77946
Range77946
Interquartile range (IQR)38973

Descriptive statistics

Standard deviation22501.50505
Coefficient of variation (CV)0.5773613798
Kurtosis-1.2
Mean38973
Median Absolute Deviation (MAD)19487
Skewness0
Sum3037828431
Variance506317729.7
2020-11-20T09:59:44.615984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
272881< 0.1%
 
88491< 0.1%
 
149941< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
68061< 0.1%
 
47591< 0.1%
 
252411< 0.1%
 
Other values (77937)77937> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
779461< 0.1%
 
779451< 0.1%
 
779441< 0.1%
 
779431< 0.1%
 
779421< 0.1%
 

Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count17179
Unique (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9116221.272095142
Minimum390396
Maximum41523276
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:44.867028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum390396
5-th percentile415920
Q1418200
median2108088
Q316353528
95-th percentile41493993.6
Maximum41523276
Range41132880
Interquartile range (IQR)15935328

Descriptive statistics

Standard deviation12041547.48
Coefficient of variation (CV)1.320892409
Kurtosis1.015063166
Mean9116221.272
Median Absolute Deviation (MAD)1692164
Skewness1.423969584
Sum7.105820995e+11
Variance1.449988656e+14
2020-11-20T09:59:45.022028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4177162380.3%
 
21096122240.3%
 
4158642140.3%
 
4175001970.3%
 
4180201780.2%
 
21095241680.2%
 
4170321660.2%
 
4169841640.2%
 
4181321590.2%
 
4180241580.2%
 
Other values (17169)7608197.6%
 
ValueCountFrequency (%) 
39039613< 0.1%
 
3952041< 0.1%
 
40180837< 0.1%
 
4021082< 0.1%
 
4021121< 0.1%
 
ValueCountFrequency (%) 
415232763< 0.1%
 
415232721< 0.1%
 
415232321< 0.1%
 
415228201< 0.1%
 
415223481< 0.1%
 

Value.1
Real number (ℝ≥0)

Distinct count77800
Unique (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1276255370.1510513
Minimum23746052
Maximum2283942049
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:45.413098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum23746052
5-th percentile896917460.9
Q11138840772
median1159657846
Q31487533073
95-th percentile1746533883
Maximum2283942049
Range2260195997
Interquartile range (IQR)348692300.5

Descriptive statistics

Standard deviation281333601.3
Coefficient of variation (CV)0.220436762
Kurtosis0.8613055934
Mean1276255370
Median Absolute Deviation (MAD)138026614
Skewness0.1632911241
Sum9.948027734e+13
Variance7.914859524e+16
2020-11-20T09:59:45.565104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
112932966811< 0.1%
 
11378386262< 0.1%
 
11498203052< 0.1%
 
11691527362< 0.1%
 
11408707202< 0.1%
 
11922845122< 0.1%
 
11313328532< 0.1%
 
11506102832< 0.1%
 
11469191182< 0.1%
 
11635321732< 0.1%
 
Other values (77790)77918> 99.9%
 
ValueCountFrequency (%) 
237460521< 0.1%
 
243755701< 0.1%
 
252100351< 0.1%
 
293908481< 0.1%
 
373873951< 0.1%
 
ValueCountFrequency (%) 
22839420491< 0.1%
 
22747212001< 0.1%
 
22740617411< 0.1%
 
22690123311< 0.1%
 
22567156401< 0.1%
 

Value.2
Real number (ℝ≥0)

Distinct count152
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28853.484752460005
Minimum28176
Maximum31152
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:45.732062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum28176
5-th percentile28272
Q128384
median29136
Q329168
95-th percentile29232
Maximum31152
Range2976
Interquartile range (IQR)784

Descriptive statistics

Standard deviation426.1425814
Coefficient of variation (CV)0.01476918941
Kurtosis-1.173215525
Mean28853.48475
Median Absolute Deviation (MAD)64
Skewness-0.1983471186
Sum2249042576
Variance181597.4997
2020-11-20T09:59:45.900065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
291681306216.8%
 
291521039213.3%
 
29136990312.7%
 
2827267828.7%
 
2918448236.2%
 
2828835454.5%
 
2825634554.4%
 
2830423303.0%
 
2841621612.8%
 
2840019642.5%
 
Other values (142)1953025.1%
 
ValueCountFrequency (%) 
281768< 0.1%
 
281923< 0.1%
 
282087< 0.1%
 
2822425< 0.1%
 
28240470.1%
 
ValueCountFrequency (%) 
311521< 0.1%
 
306723< 0.1%
 
306561< 0.1%
 
306404< 0.1%
 
306243< 0.1%
 

Value.3
Real number (ℝ≥0)

Distinct count24
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57168.35798683721
Minimum51000
Maximum74000
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:46.095099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum51000
5-th percentile52000
Q153000
median57000
Q359000
95-th percentile70000
Maximum74000
Range23000
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation4946.673313
Coefficient of variation (CV)0.0865281685
Kurtosis0.9769176759
Mean57168.35799
Median Absolute Deviation (MAD)4000
Skewness1.116634052
Sum4456102000
Variance24469576.86
2020-11-20T09:59:46.229096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
520001409418.1%
 
530001354917.4%
 
590001234415.8%
 
5800059617.6%
 
5400053366.8%
 
6000042345.4%
 
6200039565.1%
 
5700038334.9%
 
6100037874.9%
 
5600028423.6%
 
Other values (14)801110.3%
 
ValueCountFrequency (%) 
51000750.1%
 
520001409418.1%
 
530001354917.4%
 
5400053366.8%
 
5500012631.6%
 
ValueCountFrequency (%) 
740001< 0.1%
 
730001100.1%
 
720005130.7%
 
7100012221.6%
 
7000023373.0%
 

Value.4
Real number (ℝ≥0)

Distinct count77932
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133977908886.07397
Minimum5397141
Maximum262142694452
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:46.407457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5397141
5-th percentile1.486360078e+10
Q17.320097621e+10
median1.356224002e+11
Q31.948828968e+11
95-th percentile2.478215202e+11
Maximum2.621426945e+11
Range2.621372973e+11
Interquartile range (IQR)1.216819206e+11

Descriptive statistics

Standard deviation7.319727188e+10
Coefficient of variation (CV)0.5463383665
Kurtosis-1.103267144
Mean1.339779089e+11
Median Absolute Deviation (MAD)6.075476518e+10
Skewness-0.06445745204
Sum1.044317606e+16
Variance5.357840611e+21
2020-11-20T09:59:46.532457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.490201332e+1111< 0.1%
 
1.348808850e+112< 0.1%
 
2.222331557e+112< 0.1%
 
1.191583383e+112< 0.1%
 
1.257963028e+112< 0.1%
 
1.662165346e+112< 0.1%
 
1.61988909e+111< 0.1%
 
1.64997981e+111< 0.1%
 
9.262223469e+101< 0.1%
 
1.53766794e+111< 0.1%
 
Other values (77922)77922> 99.9%
 
ValueCountFrequency (%) 
53971411< 0.1%
 
95602781< 0.1%
 
100194441< 0.1%
 
167260921< 0.1%
 
199177951< 0.1%
 
ValueCountFrequency (%) 
2.621426945e+111< 0.1%
 
2.621371661e+111< 0.1%
 
2.621294977e+111< 0.1%
 
2.621282776e+111< 0.1%
 
2.62126726e+111< 0.1%
 

Value.5
Real number (ℝ≥0)

Distinct count27
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55906.80847242357
Minimum52000
Maximum78000
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:46.668451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum52000
5-th percentile53000
Q154000
median54000
Q355000
95-th percentile74000
Maximum78000
Range26000
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation5565.117394
Coefficient of variation (CV)0.09954274884
Kurtosis7.123767981
Mean55906.80847
Median Absolute Deviation (MAD)0
Skewness2.943402536
Sum4357768000
Variance30970531.61
2020-11-20T09:59:46.778473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
540003936850.5%
 
550001815323.3%
 
530001021213.1%
 
5600023623.0%
 
7500020972.7%
 
760009991.3%
 
740008601.1%
 
730004950.6%
 
770004750.6%
 
720003460.4%
 
Other values (17)25803.3%
 
ValueCountFrequency (%) 
520001510.2%
 
530001021213.1%
 
540003936850.5%
 
550001815323.3%
 
5600023623.0%
 
ValueCountFrequency (%) 
780001150.1%
 
770004750.6%
 
760009991.3%
 
7500020972.7%
 
740008601.1%
 

Value.6
Real number (ℝ≥0)

Distinct count77935
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128639075956.33882
Minimum7580852
Maximum262142274470
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:46.968474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7580852
5-th percentile1.238127874e+10
Q16.247859066e+10
median1.263877587e+11
Q31.950733345e+11
95-th percentile2.492589679e+11
Maximum2.621422745e+11
Range2.621346936e+11
Interquartile range (IQR)1.325947438e+11

Descriptive statistics

Standard deviation7.61865637e+10
Coefficient of variation (CV)0.5922505516
Kurtosis-1.20736529
Mean1.28639076e+11
Median Absolute Deviation (MAD)6.629107541e+10
Skewness0.0542063751
Sum1.002703005e+16
Variance5.804392488e+21
2020-11-20T09:59:47.435131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.760413589e+1111< 0.1%
 
1.526046569e+112< 0.1%
 
3.040007934e+102< 0.1%
 
1.570866246e+111< 0.1%
 
2583199561< 0.1%
 
7.754900356e+101< 0.1%
 
1.542806024e+111< 0.1%
 
1.207238976e+111< 0.1%
 
2.266188563e+111< 0.1%
 
1.792180919e+111< 0.1%
 
Other values (77925)77925> 99.9%
 
ValueCountFrequency (%) 
75808521< 0.1%
 
137920171< 0.1%
 
139648081< 0.1%
 
148357771< 0.1%
 
186032841< 0.1%
 
ValueCountFrequency (%) 
2.621422745e+111< 0.1%
 
2.621401329e+111< 0.1%
 
2.621394191e+111< 0.1%
 
2.621372399e+111< 0.1%
 
2.621365613e+111< 0.1%
 

Value.7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count27408
Unique (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81771749.35384299
Minimum49286532
Maximum90512212
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:47.795583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum49286532
5-th percentile49306716
Q174511082
median88786992
Q390480972
95-th percentile90500588
Maximum90512212
Range41225680
Interquartile range (IQR)15969890

Descriptive statistics

Standard deviation12068258.3
Coefficient of variation (CV)0.1475846903
Kurtosis1.016396671
Mean81771749.35
Median Absolute Deviation (MAD)1713492
Skewness-1.424393504
Sum6.373862547e+12
Variance1.456428585e+14
2020-11-20T09:59:47.982582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
90486428610.1%
 
90501564610.1%
 
90483044590.1%
 
90487260550.1%
 
90492360540.1%
 
90481884520.1%
 
89616844520.1%
 
88787148500.1%
 
90476864500.1%
 
90481028490.1%
 
Other values (27398)7740499.3%
 
ValueCountFrequency (%) 
492865321< 0.1%
 
492873368< 0.1%
 
492879161< 0.1%
 
492882041< 0.1%
 
492882842< 0.1%
 
ValueCountFrequency (%) 
9051221236< 0.1%
 
9051219615< 0.1%
 
905117004< 0.1%
 
9051154423< 0.1%
 
9051145614< 0.1%
 

Value.8
Real number (ℝ≥0)

Distinct count77680
Unique (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36304973.86002027
Minimum1227215
Maximum208921670
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:59:48.246106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1227215
5-th percentile7458813.3
Q110652824
median35547677
Q353548486.5
95-th percentile78410667.1
Maximum208921670
Range207694455
Interquartile range (IQR)42895662.5

Descriptive statistics

Standard deviation27832489.63
Coefficient of variation (CV)0.7666302072
Kurtosis1.258369254
Mean36304973.86
Median Absolute Deviation (MAD)24126885
Skewness0.9786905473
Sum2.829863797e+12
Variance7.746474792e+14
2020-11-20T09:59:48.394777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5423460711< 0.1%
 
527283152< 0.1%
 
116878672< 0.1%
 
351932302< 0.1%
 
119283682< 0.1%
 
489175472< 0.1%
 
504658042< 0.1%
 
545555462< 0.1%
 
525873862< 0.1%
 
109862462< 0.1%
 
Other values (77670)77918> 99.9%
 
ValueCountFrequency (%) 
12272151< 0.1%
 
13840041< 0.1%
 
15945281< 0.1%
 
18964671< 0.1%
 
20962491< 0.1%
 
ValueCountFrequency (%) 
2089216701< 0.1%
 
2010210551< 0.1%
 
2007504671< 0.1%
 
1977521531< 0.1%
 
1976284541< 0.1%
 

Value.9
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count1035
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.27753473514053
Minimum0
Maximum114998
Zeros70053
Zeros (%)89.9%
Memory size609.0 KiB
2020-11-20T09:59:48.556808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile163
Maximum114998
Range114998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3791.086831
Coefficient of variation (CV)15.51958855
Kurtosis540.5498207
Mean244.2775347
Median Absolute Deviation (MAD)0
Skewness22.44607899
Sum19040701
Variance14372339.36
2020-11-20T09:59:48.694299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
07005389.9%
 
1011341.5%
 
275090.7%
 
785070.7%
 
953620.5%
 
443190.4%
 
2142530.3%
 
1122370.3%
 
1462130.3%
 
1972120.3%
 
Other values (1025)41485.3%
 
ValueCountFrequency (%) 
07005389.9%
 
1011341.5%
 
2215< 0.1%
 
275090.7%
 
348< 0.1%
 
ValueCountFrequency (%) 
1149981< 0.1%
 
1111051< 0.1%
 
1106231< 0.1%
 
1072281< 0.1%
 
1064981< 0.1%
 

Value.10
Categorical

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size609.0 KiB
None
23661
AMG
12118
Kripke
9052
Quicksilver
8785
PENNANT
8718
Other values (2)
15613
ValueCountFrequency (%) 
None2366130.4%
 
AMG1211815.5%
 
Kripke905211.6%
 
Quicksilver878511.3%
 
PENNANT871811.2%
 
linpack800910.3%
 
LAMMPS76049.8%
 
2020-11-20T09:59:48.889403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length5.704619806
Min length3

Running App ID
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.332828716948696
Minimum0
Maximum6
Zeros23661
Zeros (%)30.4%
Memory size609.0 KiB
2020-11-20T09:59:49.046880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.092847849
Coefficient of variation (CV)0.8971288092
Kurtosis-1.205780872
Mean2.332828717
Median Absolute Deviation (MAD)2
Skewness0.3879968827
Sum181837
Variance4.380012119
2020-11-20T09:59:49.175007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02366130.4%
 
21211815.5%
 
1905211.6%
 
5878511.3%
 
3871811.2%
 
6800910.3%
 
476049.8%
 
ValueCountFrequency (%) 
02366130.4%
 
1905211.6%
 
21211815.5%
 
3871811.2%
 
476049.8%
 
ValueCountFrequency (%) 
6800910.3%
 
5878511.3%
 
476049.8%
 
3871811.2%
 
21211815.5%
 

Interactions

2020-11-20T09:59:06.404899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:06.689425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:06.931389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:07.205934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:07.654010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:07.910485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:08.162547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:08.394071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:08.626071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:08.869135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:09.099689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:09.343175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:09.601489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:09.901751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:10.179781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:10.423819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:10.649781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:10.866288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:11.079288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:11.291831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:11.496078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:11.695081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:11.892045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:12.124624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:12.380105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:12.662143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:12.890651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:13.258206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:13.486208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:13.706240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:13.922467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:14.130466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:14.343465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:14.543466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:15.017509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:15.333502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:15.582539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:15.830505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:16.066503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:16.289526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:16.510526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:16.770530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:17.263188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:17.624753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:18.034372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:18.297652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:18.595615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:18.830617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:19.104622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:19.789412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:20.198417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:20.488414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:20.731451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:20.955444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:21.175442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:21.392444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:21.620409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:21.825412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:22.053415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:22.330412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:22.622414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:22.893410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:23.127414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:23.350001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:23.730967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:23.955012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:24.198969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:24.413965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:24.632969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:24.885967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:25.165969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:25.459970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:25.707971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:26.019967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:26.269805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:26.881752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:27.166003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:27.486123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:27.795174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:28.053603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:28.277622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:28.487135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:28.688920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:28.894918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:29.107091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:29.351908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:29.573417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:29.822449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:30.106973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:30.386013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:30.626534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:30.851709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:31.081291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:31.303097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:31.685062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:31.909697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:32.168697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:32.464698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:32.727734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:32.958693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:33.181694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:33.398694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:33.607697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:33.828730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:34.048694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:34.262717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:34.468107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:34.680142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:34.936143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:35.217746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:35.459384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:35.672517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:35.888031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:36.104764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:36.312773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:36.515256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:36.721256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:36.918184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:37.140155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:37.392712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:37.664292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:37.931331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:38.154821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:38.369852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:38.735548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:38.965581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:39.182205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:39.385265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:39.595265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:39.828296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:40.077815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:40.347841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:40.587391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:40.834391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:41.049391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:41.262355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:41.476392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:41.693964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:41.909500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:42.140499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:42.402505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:42.660501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:42.898540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:43.125541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-20T09:59:49.323227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-20T09:59:49.749309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-20T09:59:50.289912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-20T09:59:51.006191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-20T09:59:43.486510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:59:43.972922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

BenutzerdefiniertValueValue.1Value.2Value.3Value.4Value.5Value.6Value.7Value.8Value.9Value.10Running App ID
00390396113493261828448520009221681391254000648123674669045751277954270None0
11390396162096866728448530009229321264254000648883136219045751211080102129None0
22390396153945209228448530009236888530054000649643794069045802410530033129None0
3339039613433264382844853000924445589345400065030296778904580249215962129None0
4439039610931852042844852000925181506045400065090359125904580247480787129None0
55390396161125319028448530009259528047255000651662500449045802411001029129None0
66390396152662974528448530009267263848955000652351318869045802410479566129None0
77390396148722342028448520009274992570655000653010504809045802410153395129None0
8839039612264107512844852000928252358165400065360512058904580248424095129None0
99390396157326072828448530009290214150355000654348467689045802410720785129None0

Last rows

BenutzerdefiniertValueValue.1Value.2Value.3Value.4Value.5Value.6Value.7Value.8Value.9Value.10Running App ID
7793777937493664172238907228496550001656232616115400015206728599990399188139496250None0
77938779384196169213849672848052000165686135229540001521246131849047750479066080None0
7793977939419612151669583928400530001657617770645400015220024323990477792103830360None0
779407794041959612887301962840052000165834891074550001522664945969047804088214220None0
779417794141918414945790852836853000165912275092540001523391338149047913610251292435None0
779427794241918413865130452836853000165986851012540001524018027219047913694351430None0
7794377943418884164940378528320530001660646723465400015247071172490479044112437890None0
779447794441888413054858832832052000166138767005540001525312932959047904489409310None0
7794577945418884172606189528320520001662165346285400015260465687790479044117547600None0
7794677946418884172606189528320520001662165346285400015260465687790479044117547600None0